Invasive and in situ or non-invasive components may co-exist within the same pulmonary adenocarcinoma nodule. This may result in a heterogeneous ground glass nodule (GGN) represented in a computed tomography (CT) image. Conventional approaches are unable to distinguish GGNs exhibiting minimal invasion from GGNs exhibiting frank invasion on CT. Conventional approaches rely on histopathology to definitively identify the extent of invasive adenocarcinoma (IA) from in situ or non-invasive adenocarcinoma (AIS) within a GGN.
Guidelines for managing GGNs detected in CT imagery have been proposed but are not widely implemented. These guidelines consider the size of the GGN and the size of the solid component as represented in the CT imagery. Conventional approaches may examine the relationship between the solid component size and cancer prognosis. However, conventional approaches do not map IA detected by histological examination to data provided by CT imaging.
Registering ex vivo histology slices to pulmonary CT imagery is challenging. Pulmonary tissue, or other cancerous tissue, is soft and collapses during histology preparation, causing elastic deformations and ripping of the tissue. Correspondences between histology slices and CT imagery are also difficult to identify because histology specimens are frequently obliquely sectioned without consideration of the CT reconstruction. Furthermore, in a clinical setting, only a relatively small number of histology slices may be available from a GGN, and these slices may have been frozen or prepared after fixation, causing further deformities.
Some conventional approaches have attempted to reconstruct thinly sliced lung adenocarcinoma histology slices into three dimensional (3D) models, but these 3D reconstructions are not registered to CT imagery. At least 25% of nodules identified on baseline CT imagery are GGN nodules. GGNs that persist on follow-up scans often represent early cancers that, if resected, have a five year disease free survival range of between 67% and 100%, depending on the extent of IA in the GGN. Thus, a more accurate way to distinguish IA from AIS in GGNs detected in CT imagery would facilitate improved patient outcomes and reduce un-needed invasive procedures.